Dissertations / Theses on the topic 'Macroeconomics – Forecasting'

central debates in empirical macroeconomic modeling.

Chapter 1, entitled “A Model for Real-Time Data Assessment with an Application to GDP Growth Rates”, provides a model for the data

revisions of macroeconomic variables that distinguishes between rational expectation updates and noise corrections. Thus, the model encompasses the two polar views regarding the publication process of statistical agencies: noise versus news. Most of the studies previous studies that analyze data revisions are based

on the classical noise and news regression approach introduced by Mankiew, Runkle and Shapiro (1984). The problem is that the statistical tests available do not formulate both extreme hypotheses as collectively exhaustive, as recognized by Aruoba (2008). That is, it would be possible to reject or accept both of them simultaneously. In turn, the model for the

DPP presented here allows for the simultaneous presence of both noise and news. While the “regression approach” followed by Faust et al. (2005), along the lines of Mankiew et al. (1984), identifies noise in the preliminary

figures, it is not possible for them to quantify it, as done by our model.

The second and third chapters acknowledge the possibility that macroeconomic data is measured with errors, but the approach followed to model the missmeasurement is extremely stylized and does not capture the complexity of the revision process that we describe in the first chapter.

Chapter 2, entitled “Revisiting the Success of the RBC model”, proposes the use of dynamic factor models as an alternative to the VAR based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Our empirical illustration compares the out-of-sample forecasting performance of a simple RBC model augmented with a serially correlated noise component against several specifications belonging to classes of dynamic factor and VAR models. Although the performance of the RBC model is comparable

to that of the reduced form models, a formal test of predictive accuracy reveals that the weak restrictions are more useful at forecasting than the strong behavioral assumptions imposed by the microfoundations in the model economy.

The last chapter, “What are Shocks Capturing in DSGE modeling”, contributes to current debates on the use and interpretation of larger DSGE

models. Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies point out that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin and

Sala, 2005, Uhlig, 2004). In this Chapter, we propose to reconcile both views by considering an alternative DSGE estimation approach which

models explicitly the statistical agency along the lines of Sargent (1989). This enables us to distinguish whether the exogenous shocks in DSGE

modeling are structural or instead serve the purpose of fitting the data in presence of misspecification and measurement problems. When applied to the original Smets and Wouters (2007) model, we find that the explanatory power of the structural shocks decreases at high frequencies. This allows us to back out a smoother measure of the natural output gap than that

resulting from the original specification.
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forecasting.

The issue of using data as available in real-time to forecasters, policymakers or financial

markets is an important one which has only recently been taken on board in the empirical

literature. Data available and used in real-time are preliminary and differ from ex-post

revised data, and given that data revisions may be quite substantial, the use of latest

available instead of real-time can substantially affect empirical findings (see, among others,

Croushore’s (2011) survey). Furthermore, as variables are released on different dates

and with varying degrees of publication lags, in order not to disregard timely information,

datasets are characterized by the so-called “ragged-edge”structure problem. Hence, special

econometric frameworks, such as developed by Giannone, Reichlin and Small (2008) must

be used.

The first Chapter, “The impact of macroeconomic news on bond yields: (in)stabilities over

time and relative importance”, studies the reaction of U.S. Treasury bond yields to real-time

market-based news in the daily flow of macroeconomic releases which provide most of the

relevant information on their fundamentals, i.e. the state of the economy and inflation. We

find that yields react systematically to a set of news consisting of the soft data, which have

very short publication lags, and the most timely hard data, with the employment report

being the most important release. However, sub-samples evidence reveals that parameter

instability in terms of absolute and relative size of yields response to news, as well as

significance, is present. Especially, the often cited dominance to markets of the employment

report has been evolving over time, as the size of the yields reaction to it was steadily

increasing. Moreover, over the recent crisis period there has been an overall switch in the

relative importance of soft and hard data compared to the pre-crisis period, with the latter

becoming more important even if less timely, and the scope of hard data to which markets

react has increased and is more balanced as less concentrated on the employment report.

Markets have become more reactive to news over the recent crisis period, particularly to

hard data. This is a consequence of the fact that in periods of high uncertainty (bad state),

markets starve for information and attach a higher value to the marginal information content

of these news releases.

The second and third Chapters focus on the real-time ability of models to now-and-forecast

in a data-rich environment. It uses an econometric framework, that can deal with large

panels that have a “ragged-edge”structure, and to evaluate the models in real-time, we

constructed a database of vintages for US variables reproducing the exact information that

was available to a real-time forecaster.

The second Chapter, “Real-time nowcasting of GDP: a factor model versus professional

forecasters”, performs a fully real-time nowcasting (forecasting) exercise of US real GDP

growth using Giannone, Reichlin and Smalls (2008), henceforth (GRS), dynamic factor

model (DFM) framework which enables to handle large unbalanced datasets as available

in real-time. We track the daily evolution throughout the current and next quarter of the

model nowcasting performance. Similarly to GRS’s pseudo real-time results, we find that

the precision of the nowcasts increases with information releases. Moreover, the Survey of

Professional Forecasters does not carry additional information with respect to the model,

suggesting that the often cited superiority of the former, attributable to judgment, is weak

over our sample. As one moves forward along the real-time data flow, the continuous

updating of the model provides a more precise estimate of current quarter GDP growth and

the Survey of Professional Forecasters becomes stale. These results are robust to the recent

recession period.

The last Chapter, “Real-time forecasting in a data-rich environment”, evaluates the ability

of different models, to forecast key real and nominal U.S. monthly macroeconomic variables

in a data-rich environment and from the perspective of a real-time forecaster. Among

the approaches used to forecast in a data-rich environment, we use pooling of bi-variate

forecasts which is an indirect way to exploit large cross-section and the directly pooling of

information using a high-dimensional model (DFM and Bayesian VAR). Furthermore forecasts

combination schemes are used, to overcome the choice of model specification faced by

the practitioner (e.g. which criteria to use to select the parametrization of the model), as

we seek for evidence regarding the performance of a model that is robust across specifications/

combination schemes. Our findings show that predictability of the real variables is

confined over the recent recession/crisis period. This in line with the findings of D’Agostino

and Giannone (2012) over an earlier period, that gains in relative performance of models

using large datasets over univariate models are driven by downturn periods which are characterized

by higher comovements. These results are robust to the combination schemes

or models used. A point worth mentioning is that for nowcasting GDP exploiting crosssectional

information along the real-time data flow also helps over the end of the great moderation period. Since this is a quarterly aggregate proxying the state of the economy,

monthly variables carry information content for GDP. But similarly to the findings for the

monthly variables, predictability, as measured by the gains relative to the naive random

walk model, is higher during crisis/recession period than during tranquil times. Regarding

inflation, results are stable across time, but predictability is mainly found at nowcasting

and forecasting one-month ahead, with the BVAR standing out at nowcasting. The results

show that the forecasting gains at these short horizons stem mainly from exploiting timely

information. The results also show that direct pooling of information using a high dimensional

model (DFM or BVAR) which takes into account the cross-correlation between the

variables and efficiently deals with the “ragged-edge”structure of the dataset, yields more

accurate forecasts than the indirect pooling of bi-variate forecasts/models.
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is composed of three chapters. The first two chapters are on nowcasting,

a topic that has received an increasing attention both among practitioners and

the academics especially in conjunction and in the aftermath of the 2008-2009

economic crisis. At the heart of the two chapters is the idea of exploiting the

information from data published at a higher frequency for obtaining early estimates

of the macroeconomic variable of interest. The models used to compute

the nowcasts are dynamic models conceived for handling in an efficient way

the characteristics of the data used in a real-time context, like the fact that due to the different frequencies and the non-synchronicity of the releases

the time series have in general missing data at the end of the sample. While

the first chapter uses a small model like a VAR for nowcasting Italian GDP,

the second one makes use of a dynamic factor model, more suitable to handle

medium-large data sets, for providing early estimates of the employment in

the euro area. The third chapter develops a topic only marginally touched

by the second chapter, i.e. the estimation of dynamic factor models on data characterized by block-structures.

The firrst chapter assesses the accuracy of the Italian GDP nowcasts based

on a small information set consisting of GDP itself, the industrial production

index and the Economic Sentiment Indicator. The task is carried out by using

real-time vintages of data in an out-of-sample exercise over rolling windows

of data. Beside using real-time data, the real-time setting of the exercise is

also guaranteed by updating the nowcasts according to the historical release calendar. The model used to compute the nowcasts is a mixed-frequency Vector

Autoregressive (VAR) model, cast in state-space form and estimated by

maximum likelihood. The results show that the model can provide quite accurate

early estimates of the Italian GDP growth rates not only with respect

to a naive benchmark but also with respect to a bridge model based on the

same information set and a mixed-frequency VAR with only GDP and the industrial production index.

The chapter also analyzes with some attention the role of the Economic Sentiment

Indicator, and of soft information in general. The comparison of our

mixed-frequency VAR with one with only GDP and the industrial production

index clearly shows that using soft information helps obtaining more accurate

early estimates. Evidence is also found that the advantage from using soft

information goes beyond its timeliness.

In the second chapter we focus on nowcasting the quarterly national account

employment of the euro area making use of both country-specific and

area wide information. The relevance of anticipating Eurostat estimates of

employment rests on the fact that, despite it represents an important macroeconomic

variable, euro area employment is measured at a relatively low frequency

(quarterly) and published with a considerable delay (approximately

two months and a half). Obtaining an early estimate of this variable is possible

thanks to the fact that several Member States publish employment data and

employment-related statistics in advance with respect to the Eurostat release

of the euro area employment. Data availability represents, nevertheless, a

major limit as country-level time series are in general non homogeneous, have

different starting periods and, in some cases, are very short. We construct a

data set of monthly and quarterly time series consisting of both aggregate and

country-level data on Quarterly National Account employment, employment

expectations from business surveys and Labour Force Survey employment and

unemployment. In order to perform a real time out-of-sample exercise simulating

the (pseudo) real-time availability of the data, we construct an artificial

calendar of data releases based on the effective calendar observed during the first quarter of 2012. The model used to compute the nowcasts is a dynamic

factor model allowing for mixed-frequency data, missing data at the beginning

of the sample and ragged edges typical of non synchronous data releases. Our

results show that using country-specific information as soon as it is available

allows to obtain reasonably accurate estimates of the employment of the euro

area about fifteen days before the end of the quarter.

We also look at the nowcasts of employment of the four largest Member

States. We find that (with the exception of France) augmenting the dynamic

factor model with country-specific factors provides better results than those

obtained with the model without country-specific factors.

The third chapter of the thesis deals with dynamic factor models on data

characterized by local cross-correlation due to the presence of block-structures.

The latter is modeled by introducing block-specific factors, i.e. factors that

are specific to blocks of time series. We propose an algorithm to estimate the model by (quasi) maximum likelihood and use it to run Monte Carlo

simulations to evaluate the effects of modeling or not the block-structure on

the estimates of common factors. We find two main results: first, that in finite samples modeling the block-structure, beside being interesting per se, can help

reducing the model miss-specification and getting more accurate estimates

of the common factors; second, that imposing a wrong block-structure or

imposing a block-structure when it is not present does not have negative

effects on the estimates of the common factors. These two results allow us

to conclude that it is always recommendable to model the block-structure

especially if the characteristics of the data suggest that there is one.
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The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models.

The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons.

The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.

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and econometrics of survey data. Chapters one and two analyse two aspects

of the Survey of Professional Forecasters (SPF hereafter) dataset. This survey

provides a large information on macroeconomic expectations done by the professional

forecasters and offers an opportunity to exploit a rich information set.

But it poses a challenge on how to extract the relevant information in a proper

way. The last chapter addresses the issue of analyzing the opinions on the euro

reported in the Flash Eurobaromenter dataset.

The first chapter Measuring Uncertainty and Disagreement in the European

Survey of Professional Forecasters proposes a density forecast methodology based

on the piecewise linear approximation of the individual’s forecasting histograms,

to measure uncertainty and disagreement of the professional forecasters. Since

1960 with the introduction of the SPF in the US, it has been clear that they were a

useful source of information to address the issue on how to measure disagreement

and uncertainty, without relying on macroeconomic or time series models. Direct

measures of uncertainty are seldom available, whereas many surveys report point

forecasts from a number of individual respondents. There has been a long tradition

of using measures of the dispersion of individual respondents’ point forecasts

(disagreement or consensus) as proxies for uncertainty. Unlike other surveys, the

SPF represents an exception. It directly asks for the point forecast, and for the

probability distribution, in the form of histogram, associated with the macro variables

of interest. An important issue that should be considered concerns how to

approximate individual probability densities and get accurate individual results

for disagreement and uncertainty before computing the aggregate measures. In

contrast to Zarnowitz and Lambros (1987), and Giordani and Soderlind (2003) we

overcome the problem associated with distributional assumptions of probability

density forecasts by using a non parametric approach that, instead of assuming

a functional form for the individual probability law, approximates the histogram

by a piecewise linear function. In addition, and unlike earlier works that focus on

US data, we employ European data, considering gross domestic product (GDP),

inflation and unemployment.

The second chapter Optimal Combination of Survey Forecasts is based on

a joint work with Christine De Mol and Domenico Giannone. It proposes an

approach to optimally combine survey forecasts, exploiting the whole covariance

structure among forecasters. There is a vast literature on forecast combination

methods, advocating their usefulness both from the theoretical and empirical

points of view (see e.g. the recent review by Timmermann (2006)). Surprisingly,

it appears that simple methods tend to outperform more sophisticated ones, as

shown for example by Genre et al. (2010) on the combination of the forecasts in

the SPF conducted by the European Central Bank (ECB). The main conclusion of

several studies is that the simple equal-weighted average constitutes a benchmark

that is hard to improve upon. In contrast to a great part of the literature which

does not exploit the correlation among forecasters, we take into account the full

covariance structure and we determine the optimal weights for the combination

of point forecasts as the minimizers of the mean squared forecast error (MSFE),

under the constraint that these weights are nonnegative and sum to one. We

compare our combination scheme with other methodologies in terms of forecasting

performance. Results show that the proposed optimal combination scheme is an

appropriate methodology to combine survey forecasts.

The literature on point forecast combination has been widely developed, however

there are fewer studies analyzing the issue for combination density forecast.

We extend our work considering the density forecasts combination. Moving from

the main results presented in Hall and Mitchell (2007), we propose an iterative

algorithm for computing the density weights which maximize the average logarithmic

score over the sample period. The empirical application is made for the

European GDP and inflation forecasts. Results suggest that optimal weights,

obtained via an iterative algorithm outperform the equal-weighted used by the

ECB density combinations.

The third chapter entitled Opinion surveys on the euro: a multilevel multinomial

logistic analysis outlines the multilevel aspects related to public attitudes

toward the euro. This work was motivated by the on-going debate whether the

perception of the euro among European citizenships after ten years from its introduction

was positive or negative. The aim of this work is, therefore, to disentangle

the issue of public attitudes considering either individual socio-demographic characteristics

and macroeconomic features of each country, counting each of them

as two separate levels in a single analysis. Considering a hierarchical structure

represents an advantage as it models within-country as well as between-country

relations using a single analysis. The multilevel analysis allows the consideration

of the existence of dependence between individuals within countries induced by

unobserved heterogeneity between countries, i.e. we include in the estimation

specific country characteristics not directly observable. In this chapter we empirically

investigate which individual characteristics and country specificities are

most important and affect the perception of the euro. The attitudes toward the

euro vary across individuals and countries, and are driven by personal considerations

based on the benefits and costs of using the single currency. Individual

features, such as a high level of education or living in a metropolitan area, have

a positive impact on the perception of the euro. Moreover, the country-specific

economic condition can influence individuals attitudes.
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My dissertation consists of three essays on econometric forecasting and forecast evaluation. Each essay contributes to the literature in its own way. In the first essay, I employ a common qualitative analysis framework on a widely-available consensus forecast. I evaluate the consensus forecast's performance using the Predictive Failure statistic. I find that the survey respondents provide statistically significant directional forecasts or signals. The second essay evaluates forecasts of a prominent energy policy model. This includes analysis of the forecasts, developing a rival forecasting model, and attempting to identify information to improve the policy model's forecasts. This analysis finds that the policy model generally performs well against a sophisticated time-series model. Specifically, the model appears to incorporate data dynamics appropriately, and this technique can serve as a new benchmark to evaluate the forecasts. The third essay examines the role of data revisions on models employing error correction relationships. These error correction relationships are rooted in economic theory and thus should be robust to changing economic conditions and data revisions. This essay finds that an older, popular model breaks down using newer data vintages, suggesting that data revisions and data issues can influence the stability of vector error correction models.

The first two chapters consider factor models in the context of real-time forecasting with many indicators. Using a large number of predictors offers an opportunity to exploit a rich information set and is also considered to be a more robust approach in the presence of instabilities. On the other hand, it poses a challenge of how to extract the relevant information in a parsimonious way. Recent research shows that factor models provide an answer to this problem. The fundamental assumption underlying those models is that most of the co-movement of the variables in a given dataset can be summarized by only few latent variables, the factors. This assumption seems to be warranted in the case of macroeconomic and financial data. Important theoretical foundations for large factor models were laid by Forni, Hallin, Lippi and Reichlin (2000) and Stock and Watson (2002). Since then, different versions of factor models have been applied for forecasting, structural analysis or construction of economic activity indicators. Recently, Giannone, Reichlin and Small (2008) have used a factor model to produce projections of the U.S GDP in the presence of a real-time data flow. They propose a framework that can cope with large datasets characterised by staggered and nonsynchronous data releases (sometimes referred to as “ragged edge”). This is relevant as, in practice, important indicators like GDP are released with a substantial delay and, in the meantime, more timely variables can be used to assess the current state of the economy.

The first chapter of the thesis entitled “A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP” is based on joint work with Gerhard Rünstler and applies the framework of Giannone, Reichlin and Small (2008) to the case of euro area. In particular, we are interested in the role of “soft” and “hard” data in the GDP forecast and how it is related to their timeliness.

The soft data include surveys and financial indicators and reflect market expectations. They are usually promptly available. In contrast, the hard indicators on real activity measure directly certain components of GDP (e.g. industrial production) and are published with a significant delay. We propose several measures in order to assess the role of individual or groups of series in the forecast while taking into account their respective publication lags. We find that surveys and financial data contain important information beyond the monthly real activity measures for the GDP forecasts, once their timeliness is properly accounted for.

The second chapter entitled “Maximum likelihood estimation of large factor model on datasets with arbitrary pattern of missing data” is based on joint work with Michele Modugno. It proposes a methodology for the estimation of factor models on large cross-sections with a general pattern of missing data. In contrast to Giannone, Reichlin and Small (2008), we can handle datasets that are not only characterised by a “ragged edge”, but can include e.g. mixed frequency or short history indicators. The latter is particularly relevant for the euro area or other young economies, for which many series have been compiled only since recently. We adopt the maximum likelihood approach which, apart from the flexibility with regard to the pattern of missing data, is also more efficient and allows imposing restrictions on the parameters. Applied for small factor models by e.g. Geweke (1977), Sargent and Sims (1977) or Watson and Engle (1983), it has been shown by Doz, Giannone and Reichlin (2006) to be consistent, robust and computationally feasible also in the case of large cross-sections. To circumvent the computational complexity of a direct likelihood maximisation in the case of large cross-section, Doz, Giannone and Reichlin (2006) propose to use the iterative Expectation-Maximisation (EM) algorithm (used for the small model by Watson and Engle, 1983). Our contribution is to modify the EM steps to the case of missing data and to show how to augment the model, in order to account for the serial correlation of the idiosyncratic component. In addition, we derive the link between the unexpected part of a data release and the forecast revision and illustrate how this can be used to understand the sources of the

latter in the case of simultaneous releases. We use this methodology for short-term forecasting and backdating of the euro area GDP on the basis of a large panel of monthly and quarterly data. In particular, we are able to examine the effect of quarterly variables and short history monthly series like the Purchasing Managers' surveys on the forecast.

The third chapter is entitled “Large Bayesian VARs” and is based on joint work with Domenico Giannone and Lucrezia Reichlin. It proposes an alternative approach to factor models for dealing with the curse of dimensionality, namely Bayesian shrinkage. We study Vector Autoregressions (VARs) which have the advantage over factor models in that they allow structural analysis in a natural way. We consider systems including more than 100 variables. This is the first application in the literature to estimate a VAR of this size. Apart from the forecast considerations, as argued above, the size of the information set can be also relevant for the structural analysis, see e.g. Bernanke, Boivin and Eliasz (2005), Giannone and Reichlin (2006) or Christiano, Eichenbaum and Evans (1999) for a discussion. In addition, many problems may require the study of the dynamics of many variables: many countries, sectors or regions. While we use standard priors as proposed by Litterman (1986), an

important novelty of the work is that we set the overall tightness of the prior in relation to the model size. In this we follow the recommendation by De Mol, Giannone and Reichlin (2008) who study the case of Bayesian regressions. They show that with increasing size of the model one should shrink more to avoid overfitting, but when data are collinear one is still able to extract the relevant sample information. We apply this principle in the case of VARs. We compare the large model with smaller systems in terms of forecasting performance and structural analysis of the effect of monetary policy shock. The results show that a standard Bayesian VAR model is an appropriate tool for large panels of data once the degree of shrinkage is set in relation to the model size.

The fourth chapter entitled “Forecasting euro area inflation with wavelets: extracting information from real activity and money at different scales” proposes a framework for exploiting relationships between variables at different frequency bands in the context of forecasting. This work is motivated by the on-going debate whether money provides a reliable signal for the future price developments. The empirical evidence on the leading role of money for inflation in an out-of-sample forecast framework is not very strong, see e.g. Lenza (2006) or Fisher, Lenza, Pill and Reichlin (2008). At the same time, e.g. Gerlach (2003) or Assenmacher-Wesche and Gerlach (2007, 2008) argue that money and output could affect prices at different frequencies, however their analysis is performed in-sample. In this Chapter, it is investigated empirically which frequency bands and for which variables are the most relevant for the out-of-sample forecast of inflation when the information from prices, money and real activity is considered. To extract different frequency components from a series a wavelet transform is applied. It provides a simple and intuitive framework for band-pass filtering and allows a decomposition of series into different frequency bands. Its application in the multivariate out-of-sample forecast is novel in the literature. The results indicate that, indeed, different scales of money, prices and GDP can be relevant for the inflation forecast.

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